Statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
A present day challenge is to evaluate the lifecycle of constituent components for complex products or systems using large scale historical databases. Such large scale historical databases may have involved rules based systems, relational databases and query systems, data mining systems and processes, and even human hand analysis. Each of these systems or methods has drawbacks that limit their effectiveness in assisting with rapid non-conformance analysis of products and systems. Non-conformance may include any condition that is at variance with a nominal condition. For example, modern systems and processes are well known and can handle extensive amounts of data. Modern systems often involve data mining solutions that employ predictive data mining techniques such as text mining and clustering. Such data mining solutions can handle large data sets by summarizing them into usable chunks. However, such systems do not allow for whole text capture and are thus limited in their ability to relate entities in a complex and subtle manner.
A specific drawback of modern data mining solutions is that they are typically reductive and can lose a good deal of information. More specifically, these reductive solutions tend to lose the subtleties of the data that is often important in determining the desirable patterns that do not repeat often. Modern data mining solutions can also be time consuming and costly in terms of manpower hours, as well as being CPU intensive. Much of the association data between entities is lost, because one is “forced” into characterizing an entity by pre-defined characteristics. For example, many database systems have drop-down menus that allow only certain categories to be chosen. The categories may not contain enough detail to adequately address all the associations between entities, therefore omitting relationships that can be of significant assistance in determining a desired result or assisting in an analytical process. For example a relational database might force a problem to be described as “connector did not work”. However, free text might be used to describe the problem as “connector pin bent” and another problem might say “connector not tightened sufficiently”. It is only in the free text that the true nature of the problem can be described. Rules based systems also tend to be fragile and non-reactive to changing business conditions.
Large scale historical databases often have other drawbacks in addition to those described above. For example, large scale historical databases typically contain the system's problem reports, operational histories, process notes and part material codes, etc., and can be extensive in size and reside in multiple, different databases. In addition, such large scale historical databases often contain textual content that is often input by a multitude of designers, producers, operators, technicians, maintenance personnel and other contributors. As a result, differences in documentation approach, the terminology and vernacular used to describe the non-conformance, and spelling are very common.
Thus, there exists a continual challenge to extract actionable information from large volumes of current and historical free text, which leads to a multitude of correlation issues that add to the complexity of lifecycle analysis. This can result in a plethora of computational and analytic problems. The usual result is long analysis mitigation times which lead to high costs, which can be very burdensome, if not unacceptable, for many businesses and governmental operations. Low-cost recurring problems on medium and high complexity systems can often be difficult to discover simply because of the significant time required to perform non-conformance investigations with traditional large scale historical databases.